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A hybrid CNN-LSTM model is trained to localise anomalies in each channel of EEG record. Proposed architecture is divided into two steps. First, Deep CNN is trained for detecting abnormal channels. Furthermore, to detect anomaly time from abnormal channels Long Short-Term Memory (LSTM) network is trained.

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tayyabafaysal/CNN-LSTM-EEG

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README.md

README.md

 
 

deep_cnn_arhictecture.py

deep_cnn_arhictecture.py

 
 

eeg_data_preprocessing.py

eeg_data_preprocessing.py

 
 

feature_extraction.py

feature_extraction.py

 
 

histogram.py

histogram.py

 
 

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CNN-LSTM-EEG

Overview

A hybrid CNN-LSTM model is trained to localise anomalies in each channel of EEG record. Proposed architecture is divided into two steps. First, Deep CNN is trained for detecting abnormal channels. Furthermore, to detect anomaly time from abnormal channels Long Short-Term Memory (LSTM) network is trained.

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This application was programmed in Python 3.5

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A hybrid CNN-LSTM model is trained to localise anomalies in each channel of EEG record. Proposed architecture is divided into two steps. First, Deep CNN is trained for detecting abnormal channels. Furthermore, to detect anomaly time from abnormal channels Long Short-Term Memory (LSTM) network is trained.

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